Estimating accurate intermediate flows are critical for the VFI task. In our method, reliable intermediate flows can better guide the learning process of the subsequent DConv layer. In this part, we investigate the performance of different intermediate flow estimation strategies. In Table REF , we compare four strategies, i.e., linear combination [1]}, flow projection [2]}, task-oriented [3]} and our distillation plus task-oriented strategy. The first two methods employ PWC-Net [4]} as the basic flow estimation network and then predict the intermediate flows using the linear combination and flow projection strategies respectively. The next two methods use our coarse-to-fine flow estimation network to predict the intermediate flow directly. All the models are trained by \(2\times 10^{5}\)  iterations. As listed in the table, the task-oriented strategy is better than the linear combination and flow projection strategies. Besides, the distillation strategy can further improve performance significantly.
